A data-driven typology of individually perceived workplace environments and employee well-being.

ABSTRACT: This study adopts a data-driven, employee-centered approach to explore how individually perceived workplace environments shape well-being. Analyzing over 1.3 million observations from 2017 to 2021, we identify nine key job demand and resource factors-including workload, emotional burden, job autonomy, organizational integrity, and support-using factor analysis. Rather than examining these dimensions in isolation, we apply Gaussian mixture models (GMM) to uncover ten distinct workplace types that reflect co-occurring patterns of demands and resources as experienced by individual employees, ranging from Grade D (least favorable) to Grade A1 (most favorable). Our findings reveal substantial variation in employee well-being-including mental health, work engagement, job satisfaction, and workpl

SUBMITTER: Xie J 

PROVIDER: S-EPMC12475295 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature